Papers
2
Total Citations
12
H-Index
2
About
Zongfang Ma is a researcher at the forefront of intelligent robotics and autonomous driving systems, with a focus on developing efficient, real-world solutions for navigation and perception. Her work bridges the gap between theoretical algorithms and practical deployment on resource-constrained hardware. She has made significant contributions to hybrid path planning, introducing an innovative method that combines an improved A* algorithm with Timed Elastic Band (TEB) optimization to enable safe and smooth navigation in both static and dynamic environments. This work, published in 2024, has already garnered 10 citations, reflecting its immediate relevance to the robotics community. In the domain of computer vision, Ma developed an end-to-end, lightweight deep learning method for license plate detection and recognition, specifically designed for mobile edge computing chips with limited computational power. This approach, cited 2 times, addresses a critical bottleneck in deploying vision systems on embedded platforms. Her research is characterized by a practical, systems-level perspective, ensuring that advanced algorithms can be effectively implemented in real-world autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2